“Corsework2”

Libraries

First we are going to upload the libraries

library(tmap)
library(geojsonio)
library(sf)
library(raster)
library(tidyverse)
library(spData)
library(spatstat)
library(sp)
library(raster)

Then we are going to read the data from Adam’s Dropbox for the route.

hunt <- geojson_read("https://www.dropbox.com/s/wa2ip35tcmt93g3/Team7.geojson?raw=1", what = "sp")

Let’s have a look at the data.

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(hunt) +
  tm_lines (col = "green", lwd = 4)

sooooo we converted sp to sf …..we measure the distance and we change the coordinate system

st_as_sf ——st means is the generic of simple features, therefore we are transforming from sp to sf

hunt1 <- st_as_sf(hunt)
st_crs(hunt1)

## Coordinate Reference System:
##   EPSG: 4326 
##   proj4string: "+proj=longlat +datum=WGS84 +no_defs"

we should take a look at the projection we also can ask to the head of our data simple features dont work well with rasters

head(hunt1)

## Simple feature collection with 1 feature and 11 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: -0.1359 ymin: 51.47773 xmax: 0.033067 ymax: 51.54356
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##          Name         descriptio timestamp begin  end altitudeMo
## 0 Casa team 7 2016 treasure hunt      <NA>  <NA> <NA>       <NA>
##   tessellate extrude visibility drawOrder icon
## 0          1       0         -1      <NA> <NA>
##                         geometry
## 0 LINESTRING (-0.1359 51.5221...

Let’s measure the distance

st_length(hunt1)

## 46610.68 m

Let’s change to the national british grid

hunt2 <- st_transform(hunt1, 27700)
st_crs

## function (x, ...) 
## UseMethod("st_crs")
## <bytecode: 0x000000001cd44168>
## <environment: namespace:sf>

library(rgdal)

Read tube statations file as simple features

tubestations <- st_read("https://www.doogal.co.uk/LondonStationsKML.ashx","London stations with zone information")

## Reading layer `London stations with zone information' from data source `https://www.doogal.co.uk/LondonStationsKML.ashx' using driver `KML'
## Simple feature collection with 641 features and 2 fields
## geometry type:  POINT
## dimension:      XYZ
## bbox:           xmin: -0.61143 ymin: 51.28216 xmax: 0.329952 ymax: 51.74702
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

Change to National British grid and plot, we can confirm ploting the head of the data.

Let’s plot the stations.

stations1<-  st_transform(tubestations, 27700) 
head(stations1)

## Simple feature collection with 6 features and 2 fields
## geometry type:  POINT
## dimension:      XYZ
## bbox:           xmin: 519457 ymin: 163744 xmax: 547297 ymax: 183352
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
##                Name
## 1        Abbey Road
## 2        Abbey Wood
## 3     Acton Central
## 4   Acton Main Line
## 5        Acton Town
## 6 Addington Village
##                                                                                                                                                                                                        Description
## 1               <div>Zone 3</div> <div>E15 3NB</div> <div>Latitude: 51.531952</div> <div>Longitude: 0.003723</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Abbey Road">More info</a> </div>
## 2               <div>Zone 4</div> <div>SE2 9RH</div> <div>Latitude: 51.490784</div> <div>Longitude: 0.120272</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Abbey Wood">More info</a> </div>
## 3             <div>Zone 2</div> <div>W3 6BD</div> <div>Latitude: 51.508758</div> <div>Longitude: -0.26343</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Acton Central">More info</a> </div>
## 4           <div>Zone 3</div> <div>W3 0BP</div> <div>Latitude: 51.516887</div> <div>Longitude: -0.26769</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Acton Main Line">More info</a> </div>
## 5               <div>Zone 3</div> <div>W3 8HN</div> <div>Latitude: 51.503071</div> <div>Longitude: -0.280303</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Acton Town">More info</a> </div>
## 6 <div>Zone 3,4,5,6</div> <div>CR0 5AR</div> <div>Latitude: 51.356239</div> <div>Longitude: -0.032665</div> <div> <a href="https://www.doogal.co.uk/StationMap.php?station=Addington Village">More info</a> </div>
##                         geometry
## 1 POINT Z (539081 183352 -45....
## 2 POINT Z (547297 179002 -45....
## 3 POINT Z (520613 180299.1 -4...
## 4 POINT Z (520296 181196.1 -4...
## 5 POINT Z (519457 179639 -46....
## 6 POINT Z (537082 163744 -45....

tm_shape(stations1) +
  tm_dots(col="blue")

### Next thing is the 100m distance buffer for the route.

hunt3 <- st_buffer(hunt2, dist = 100)
class(hunt2)

## [1] "sf"         "data.frame"

tm_shape(hunt3) +
  tm_polygons(col = "red")

### super buffer is done ### now we should plot the tube stations

library(tidyverse)

Read the hunt addresses from a csv file and check the head of the data

huntaddresses <-  read.csv("https://www.dropbox.com/s/2cbu2ux9ddy9c0l/huntaddresses.csv?raw=1")
head(huntaddresses)

##                              Location Points      lat        lon
## 1            Picadilly Circus, London      2 51.51009 -0.1345676
## 2             Nelson's Column, London      2 51.50775 -0.1279302
## 3                     Big Ben, London      2 51.50073 -0.1246254
## 4     100 Club, Oxford Street, London      5 51.51615 -0.1352761
## 5 Fabric, Charterhouse Street, London      5 51.51959 -0.1024735
## 6 The Gherkin, 30 St Mary Axe, London      2 51.51449 -0.0803065
##                                           GoogleAddress
## 1                 Piccadilly Circus, London W1D 7ET, UK
## 2               5 Trafalgar Square, London WC2N 5NJ, UK
## 3                      Westminster, London SW1A 0AA, UK
## 4          100 Oxford St, Fitzrovia, London W1D 1LL, UK
## 5 77A Charterhouse St, Clerkenwell, London EC1M 6HJ, UK
## 6                   30 St Mary Axe, London EC3A 8EP, UK

I was almost on the finish line

Harry Potter’s platform is giving a headache. the platform 9 3/4 has an address from the US let’s change it.

This is gonna work

huntaddresses[29,3] <- 51.531427
huntaddresses[29,4] <- -0.1261330

Here we transform the format of the hunt addresses to simple features.

huntaddresses1 <-  st_as_sf(huntaddresses, coords = c("lon","lat"))
head

## standardGeneric for "head" defined from package "utils"
## 
## function (x, ...) 
## standardGeneric("head")
## <environment: 0x00000000223fd6b0>
## Methods may be defined for arguments: x
## Use  showMethods("head")  for currently available ones.

Transform into a national BNG

st_crs(huntaddresses1)

## Coordinate Reference System: NA

huntadresses2 <- st_set_crs(huntaddresses1, 4326)

It doesnt have a projection we can use this function st_set_crs let’s have a look it has a WGS84 let’s change to NBG, plot the head of the data, and plot the points.

st_crs(huntadresses2)

## Coordinate Reference System:
##   EPSG: 4326 
##   proj4string: "+proj=longlat +datum=WGS84 +no_defs"

huntaddresses3 <- st_transform(huntadresses2, 27700)
head(huntaddresses3)

## Simple feature collection with 6 features and 3 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 529485.2 ymin: 179643.9 xmax: 533303.9 ymax: 181780.4
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
##                              Location Points
## 1            Picadilly Circus, London      2
## 2             Nelson's Column, London      2
## 3                     Big Ben, London      2
## 4     100 Club, Oxford Street, London      5
## 5 Fabric, Charterhouse Street, London      5
## 6 The Gherkin, 30 St Mary Axe, London      2
##                                           GoogleAddress
## 1                 Piccadilly Circus, London W1D 7ET, UK
## 2               5 Trafalgar Square, London WC2N 5NJ, UK
## 3                      Westminster, London SW1A 0AA, UK
## 4          100 Oxford St, Fitzrovia, London W1D 1LL, UK
## 5 77A Charterhouse St, Clerkenwell, London EC1M 6HJ, UK
## 6                   30 St Mary Axe, London EC3A 8EP, UK
##                    geometry
## 1 POINT (529551.5 180667.4)
## 2   POINT (530018.7 180419)
## 3 POINT (530268.1 179643.9)
## 4 POINT (529485.2 181339.4)
## 5 POINT (531751.1 181780.4)
## 6 POINT (533303.9 181254.1)

tm_shape(huntaddresses3) +
  tm_dots(col = "green")

Here comes the good the intersections.

So I have the hunt3 which is the buffer and huntaddresses3 which is the tube stations

Let’s do the intersection 1 for question 2

findtubestations = st_intersection(hunt3,stations1)

## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries

Let’s plot the intersection

tm_shape(hunt3)+
  tm_polygons(col='green')+
  tm_shape(findtubestations)+
  tm_dots(col="blue")

### This buffer is for question number 3

hunt4 <- st_buffer(hunt2, dist = 300)
tm_shape(hunt4) +
  tm_polygons(col = "red")

### new intersection this intersection is for the question number 3

findhunts = st_intersection(hunt4, huntaddresses3)

## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries

tm_shape(hunt4)+
  tm_polygons(col = "blue")+
  tm_shape(findhunts)+
  tm_dots(col="red")

### clip the data to a single object ### Let’s go to the new question number 4 now we load the london wards shapefile.

library(rgdal)

Read the shape file with OGR, transform to simple features and change to the NBG.

LondonWards <- readOGR("London Wards/LondonWards.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\Ricardo\Desktop\R Assessments\Week7 Assessment\London Wards\LondonWards.shp", layer: "LondonWards"
## with 625 features
## It has 77 fields

LondonWards1 <- st_as_sf(LondonWards)
LondonWards2<-st_transform(LondonWards1,27700)

Calculate the intersection between wards

londonWardsIntersect<- LondonWards2[hunt2,]
qtm(LondonWards2)

qtm(londonWardsIntersect)

To be more clear plot the route and the wards

tm_shape (londonWardsIntersect)+
  tm_polygons(col = NA, alpha = 0.9)+
  tm_shape(hunt) +
  tm_lines (col = "green", lwd = 4)

This is for question 4

Let’s do some filtering ——filter the data and get the values filter male and female expectancy

londonWardsIntersect$MaleLE0509

##  [1] 81.0 79.3 80.4 76.9 79.5 76.7 79.2 77.0 77.0 75.1 75.6 76.2 79.8 76.8
## [15] 78.6 78.9 74.8 77.8 75.0 78.0 76.2 75.3 80.9 78.9 76.9 76.3 74.8 81.8
## [29] 81.9 82.8

londonWardsIntersect$FemaleLE05

##  [1] 84.8 88.8 86.1 82.4 82.1 84.6 88.2 83.1 81.0 81.9 86.9 82.4 81.7 85.2
## [15] 82.7 81.5 83.0 84.9 81.3 81.9 78.2 81.4 82.7 83.0 83.8 81.7 83.5 86.0
## [29] 85.8 86.2

Get the maximun value

londonwardsmax<-filter(londonWardsIntersect,MaleLE0509==max(londonWardsIntersect$MaleLE0509))

Get the minimun value

londonwardsmin<-filter(londonWardsIntersect,MaleLE0509==min(londonWardsIntersect$MaleLE0509))
print (londonwardsmax)

## Simple feature collection with 1 feature and 77 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 530967.7 ymin: 180406.7 xmax: 533839.6 ymax: 182206.1
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
##      WD11CD WD11CDO         WD11NM WD11NMW       WardName OldCode
## 1 E09000001    00AA City of London    <NA> City of London    00AA
##    Wardcode Pop2013 Aged0_15 Aged16_64 Aged65plus PctAged0_1 PctAged16_
## 1 E09000001    8000      600      6200       1200        7.5       77.5
##   PctAged65p MeanAge201 MedianAge2 AreaSqKM PopDensity PctBame PctNotBorn
## 1         15   41.30389         39  3.15148   2538.062    21.4       36.7
##   PctNoEngli GenFertRat MaleLE0509 FemaleLE05 PctYr1Obes PctYr6Obes
## 1       11.9         34       82.8       86.2        n/a        n/a
##   RateAmbula RatesAmbul RoadKilled InEmployme Employment NoJobs2011
## 1      811.5   18.34286         58       4747   75.26558      4e+05
##   EmpWkAgePo RateNINoFo MeanHouseP NoProperti NoHousehol PctDetache
## 1   64.51613   138.8889     595000        195       5530        0.4
##   PctSemiDet PctTerrace PctFlatMai PctOwned20 PctSocialR PctPrivate
## 1        0.2        1.4         98       42.3       16.5       35.9
##   PctCTaxBan PctCTaxB_1 PctCTaxB_2 Incapacity IncomeSupp EmpSupport
## 1        4.1       63.1       32.8  0.8474576  0.5084746   1.355932
##   JSAClaiman PctDepChil PctHHNoAdu PctLonePar IDRankLond IDPctWorst
## 1   1.943365         10  0.8665906   24.17582        551         20
##   AvgGCSE201 UnauthAbse PctWithNoQ PctLev4Qua CrimeRate1 ViolenceRa
## 1        367        0.5        6.7       68.4   730.7895   68.02632
##   RobberyRat TheftAndHa CriminalDa DrugsRate1 Deliberate PctOpenSpa
## 1   6.447368   445.1316   29.73684   65.52632  0.7792208    18.6425
##   CarsPerHH2 AvgPubTran PctTTWBike TurnoutMay Shape_Leng Shape_Area  ID
## 1  0.3858609        7.6   5.308616   48.52365   9601.643    3149381 625
##        x      y                       geometry
## 1 532462 181223 POLYGON ((532140.3 182191.9...

print (londonwardsmin)

## Simple feature collection with 2 features and 77 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 533478.3 ymin: 181723.6 xmax: 535646.4 ymax: 183144.6
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
##      WD11CD WD11CDO              WD11NM WD11NMW
## 1 E05000574  00BGFX Bethnal Green South    <NA>
## 2 E05000588  00BGGM             Weavers    <NA>
##                              WardName OldCode  Wardcode Pop2013 Aged0_15
## 1 Tower Hamlets - Bethnal Green South  00BGFX E05000574   14700     2800
## 2             Tower Hamlets - Weavers  00BGGM E05000588   14150     2350
##   Aged16_64 Aged65plus PctAged0_1 PctAged16_ PctAged65p MeanAge201
## 1     11200        750       19.0       76.2        5.1       30.2
## 2     10850        950       16.6       76.7        6.7       32.7
##   MedianAge2 AreaSqKM PopDensity PctBame PctNotBorn PctNoEngli GenFertRat
## 1         28      0.9    16333.3    64.3       45.5       23.1         57
## 2         30      0.8    17687.5    47.9       41.2       16.7         46
##   MaleLE0509 FemaleLE05   PctYr1Obes   PctYr6Obes RateAmbula RatesAmbul
## 1       74.8       83.0 15.573770492 22.802850356   133.5374   1.105882
## 2       74.8       83.5 17.058823529 25.776397516   127.3498   1.155280
##   RoadKilled InEmployme Employment NoJobs2011 EmpWkAgePo RateNINoFo
## 1         18       5978   54.12894       3500  0.3125000   90.26549
## 2          9       6754   64.66871       4300  0.3963134   74.57627
##   MeanHouseP NoProperti NoHousehol PctDetache PctSemiDet PctTerrace
## 1     281000         89       5417        1.0        2.0        7.9
## 2     380000        118       5696        0.7        1.2       11.2
##   PctFlatMai PctOwned20 PctSocialR PctPrivate PctCTaxBan PctCTaxB_1
## 1       89.0       21.2       44.9       31.5   31.05656   67.64497
## 2       86.9       24.3       43.4       30.3   32.33219   67.86684
##   PctCTaxB_2 Incapacity IncomeSupp EmpSupport JSAClaiman PctDepChil
## 1   1.760939   2.142857   3.526786   4.776786   9.634944   38.93939
## 2   1.628370   2.073733   3.179724   4.654378  10.681299   39.07407
##   PctHHNoAdu PctLonePar IDRankLond IDPctWorst AvgGCSE201 UnauthAbse
## 1   8.619375   71.08434         36        100        345        0.9
## 2   6.962139   62.98343         19        100        342        0.8
##   PctWithNoQ PctLev4Qua CrimeRate1 ViolenceRa RobberyRat TheftAndHa
## 1       21.4       35.5      136.0   28.59107   8.041237   50.79038
## 2       20.6       43.9      172.2   25.79710   8.695652   77.17391
##   CriminalDa DrugsRate1 Deliberate PctOpenSpa CarsPerHH2 AvgPubTran
## 1   9.896907   18.14433        1.2    16.8688  0.3371472        6.7
## 2   9.420290   32.10145        0.7    14.9729  0.3326754        5.0
##   PctTTWBike TurnoutMay Shape_Leng Shape_Area  ID      x      y
## 1   7.811977   35.62665   3858.472   881380.6 549 534921 182290
## 2  12.052117   39.77031   3589.704   778717.6 563 533973 182571
##                         geometry
## 1 POLYGON ((535646.4 182137.3...
## 2 POLYGON ((534355.7 182614.3...

This is for question number 5

get the mean of Life expentancy male

londonwardsaveragemale<-group_by(londonWardsIntersect)%>%
                    summarise(mean_life = mean (MaleLE0509, na.rm = TRUE))     
print(londonwardsaveragemale)

## Simple feature collection with 1 feature and 1 field
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 527890.8 ymin: 176208.9 xmax: 544649.4 ymax: 185624
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
## # A tibble: 1 x 2
##   mean_life                                                       geometry
##       <dbl>                                                  <POLYGON [m]>
## 1      78.0 ((542176.5 180709, 542229.6 180707.9, 542437 180703.3, 542977~

londonwardsaveragefemale<-group_by(londonWardsIntersect)%>%
                    summarise(mean_life = mean (FemaleLE05, na.rm = TRUE))     
print(londonwardsaveragefemale)

## Simple feature collection with 1 feature and 1 field
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 527890.8 ymin: 176208.9 xmax: 544649.4 ymax: 185624
## epsg (SRID):    27700
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
## # A tibble: 1 x 2
##   mean_life                                                       geometry
##       <dbl>                                                  <POLYGON [m]>
## 1      83.6 ((542176.5 180709, 542229.6 180707.9, 542437 180703.3, 542977~

For question five let’s run a K test

Let’s Transform to a Spatial Polygons

superhunt <- huntaddresses3

First we change the points from sf to sp, now we do the opposite.

score <- sf:::as_Spatial(superhunt)

Plot the the window for the K test

window <- as.owin(LondonWards)
plot(window)

Now we should create a ppp object

huntpoints <- score
huntpoints.ppp <- ppp(x=huntpoints@coords[,1],y=huntpoints@coords[,2],window=window)

## Warning: data contain duplicated points

plot (huntpoints.ppp,pch=16,cex=1,main="huntpoints")

Finally we run our K test

#K test

K <- Kest(huntpoints.ppp, correction="border")
plot(K)